Posted by LabsLucas 2 hours ago
Hardware is the exact same as what used to be available for $2K last year (and is still $1K cheaper from Chinese OEMs).
LTT Lab's LLM testing is getting more sophisticated, which is great - I think it's worth noting that ROCm/Vulkan versions and llama.cpp build versions are going to have some big differences for numbers.
For those wanting to get the most out of their Strix Halos, there's both kernel tweaks and utilities like ryzenadj that can help you get the most out of it. ( http://strixhalo.wiki/ has most of that documented). Also, if you're running for coding or agentic work, if you model supports MTP, that's mature and should give you a decent (30%?) decode boost.
It has the same 256 GB/s memory bandwidth limit as every board previously, not sure why this is even being released right now as if it's some new fangled thing - you can go get a Framework Desktop for roughly the same price or a GMKtec EVO-X2 for a bit cheaper.
I fear that by the time the RTX Spark comes out it'd have to be $6k, and by the time a 128gb or more machine with 700+ GB/s comes out it'd be at $10k, way out of most consumers' hands.
Edit: capitalized gb/s to GB/s.
Waiting for the market to be less insane is somewhat akin to waiting for the s&p500 to drop a decent amount so you can buy in.
lol this is so wrong it's funny - equities go up in price, commodity goods go down in price. the two markets are literally diametrically opposed.
But when they cost the same price (unless the Spark has shot up too), there's no reason to buy this over a Spark.
The Spark is literally a faster version of this, with better software support.
Edit: And I say that as an owner of a Ryzen AI Max 395 device.
You'll need a custom-built distro image, but that goes for like 90% of ARM hardware on Linux.
For anyone considering these devices, the only reason I would recommend against them is if you plan on getting multiple to link together - the DGX Spark has a much, much faster interconnect bandwidth ceiling than the AMD devices do.
Otherwise, they're great!
For this to be compelling it would need to be eg 256GB minimum or something
The only problem, you need 8 or 16 memory controllers. Memory controllers are not that expensive: Intel Core i3-14100F has 2 channel controller and costs $110, so we can estimate that 16-channel controller should cost not more than $880, and 8-channel controller should cost $440.
So isn't it better to make a cheap CPU with 16 DRAM controllers instead of this $4K gear having only 128 Gb? Or maybe 2 CPUs each having 8 RAM channels?
DDR5 costs 2 times more ($360 for 32 Gb) while not even having 2 times the bandwidth so it is not worth buying. It is more reasonable to make more RAM channels and stuff them with DDR4.
This is one of the main reasons (the other is the number of PCIe lanes) why high end desktop and server CPUs have like double the number of pins and so much bigger sockets as compared to consumer desktop CPUs.
Isn't adding pins kind of expensive?
However for local single-user setups, it's often better to have access to more capable/bigger MoE models at reasonable speeds and lower concurrences, which is enabled by these platforms.
I do (and have historically done) quite a work with both local LLMs and local diffusion models. I have an M3 Max MBP at 400 GB/s and also a desktop with a RTX 4090 with 1,008 GB/s
While the M3 Max MBP can serve up MoE reasonably fast (~60 token/sec)the RTX 4090 is an entirely different experience (~170 token/sec). I also do a fair bit of experimentation and am currently running a custom decoder that requires expensive look-ahead, but I'm still able to get a usable 25 token/s on the RTX.
The raison d'etre for the DGX spark is not practical home inference, but rather offering the same fundamental architecture as data center cards for a affordable CUDA prototyping. If you want to build software to run on H100s, you probably can't justify buying (and running) a single card. The DGX spark solves this by having the same fundamental setup as what those cards have.
That makes these non-NVIDIA DGX-like devices confusing to me. The entire benefit of the DGX series is the NVIDIA architecture itself.
Anyone interested in home LLMs should decide whether a Mac or a dedicated GPU is the more sensible path based on their budget and other computer use. Each has their own benefits.
And if your jobs do fit onto a 24GB card, then you are not the target user for the "AI mini PC" niche that these guys are trying to carve out
it allows you to run smaller models much better
imo 3090s make the most sense if you can buy at least 2x ideally 4x but of course we're talking about a completely different budget at that point
It's great to get lots of tokens, but being able to handle and extent context is why it'll continue to be a great machine compared to any of the small graphics cards.
128 bit: 96 GB?
256 bit: 192 GB
512 bit: 384 GB?
1024 bit: 768 GB?
https://community.frame.work/t/was-there-no-possible-way-to-...
> A shame, really, as the Ryzen 7640U, 7840U, 7840HS, and 7940HS all support 256GB of RAM.
To be fair, those platforms support dual dimms per channel, which Strix Halo would not, at least not at it's high speeds.
But reciprocally Gorgon Halo 400 just launched and it supports... 192GB. And is the exact same APU.
Memory chips did finally have their first big doubling per chip semi recently (available last February), with 48 & 64GB dimms becoming available. There is some reasonable lag here, that Strix Halo & Gorgon Halonuse lpddr5x, which perhaps had some lag, that 32GB (x4) was the best available. But now with Gorgon Halo being 192GB capable but not 256GB, it sure feels looks & seems like this is just bad spirited fuckery from AMD. https://forum.level1techs.com/t/where-are-the-ddr5-unbuffere...